A deep policy network \(\pi _\theta ^{BC}\) , as presented in Figure REF , was then trained by treating the action selection as a classification problem.
We train \(\pi _\theta ^{BC}\)  using the negative log-likelihood over the 8117 labels that correspond to the available actions.
Note that, despite the large number of available labels, since we apply the action-space shaping as presented in Section REF , effectively only a limited number of action in the order of few dozens are to be chosen for any given state.
The network is optimized using the RAdam optimizer [1]}, a learning-rate of 0.0001, a batch size of 4 and for 30 epochs. The best performance on the validation set is obtained after 12 epochs, as shown in Figure REF . No hyperparameter tuning was performed at this stage.
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